CADDIS Volume 1

Listing candidate causes requires balancing two issues. If you include every possible stressor the causal analysis will be burdensome, but being selective in this step may eliminate the true cause. There are no rigorous rules for balancing those concerns, but the following are some suggested rules of thumb for listing candidate causes.

For Including Candidate Causes

Do:

Include candidate causes that are suggested by a manager or stakeholder. They may be right, and if they are wrong they will need to be convinced.

Include candidate causes for which there is some evidence of its possible occurrence. Volume 2 describes observations for common stressors that would suggest including them in the list.

Include things that are common causes of the observed biological effect in your state or region. Do not forget the usual suspects. At first, this will be based on professional experience, but later common causes may be identified by analysis of regional data or prior causal analyses.

If you think it is possible but are uncertain, include it. It is better to include a possible cause than to impart bias to the assessment.

Think about what may be uncommon or unique about the impaired site. It is tempting to stick with commonly occurring stressors such as low dissolved oxygen and elevated temperature, but surprising causes are common.

Think about the natural history of the impaired organisms. Fish are exposed to different stressors in different ways than macroinvertebrates or periphyton, which may suggest inclusion of different candidate causes. If the biological impairment is specific, such as absence of certain species of mussels while other species thrive, becoming familiar with their natural history and reproduction may lead you to consider causes unique to the impairment, such as symbiotic species, pathogens or habitat preferences.

Pay attention to the type and magnitude of observed effects. Some effects are more often associated with a particular cause or type of cause. For example, in the Mid-Atlantic region, a high likelihood of a poor macroinvertebrate index score was associated with poor quality sediment, acid deposition and mine drainage. Although poor algal assemblage scores were also associated with poor quality sediment, they were associated more with high nitrogen and phosphorus levels than with acid deposition or mine drainage (Figure 27 in U.S. EPA 2006).

For Excluding Candidate Causes

Do:

Exclude things that can be confidently eliminated without quantitative analysis and without controversy. Evidence that can clearly and easily exclude stressors should be used, but with great caution.

Exclude constituents of natural background water chemistry or habitat, even if they interact with an anthropogenic cause. For example, if low pH and metals are suspected to be interacting to cause effects, but low pH (referring to the "power of hydrogen") occurs throughout the watershed because of natural factors, then only the metals need be treated as a candidate cause. It is important to distinguish causes from the environmental context in which they operate. (This advice is based on the assumption that you are determining the cause of an impairment. If you are attempting to account for the condition of a population or community, natural conditions must be considered. It also does not preclude consideration of exceptional natural events such as droughts or floods that are not background conditions and can cause an apparent impairment.)

Exclude candidate causes that are at an inappropriate spatial or temporal scale for the current decision. For example, a causal analysis of a local impairment in support of a total maximum daily load (or TMDL) may defer consideration of region-wide increases in temperature. The presence of regionally or globally distributed causes should not prevent you from identifying local causes that can be remedied.

Don't:

Do not exclude a stressor based on comparing site concentrations with effects benchmarks (e.g., Ambient Water Quality Criteria). Criteria and other effects benchmarks are intended to protect most species most of the time, but they may not be applicable to a particular effect or site and sampling may miss periods of high concentrations.

Do not exclude a stressor based on its concentration in or absence from grab samples. You may be sampling at the wrong time of day or the wrong place or the stressor may occur episodically.

Do not exclude a stressor for lack of data. Causal stressors often are not regularly monitored.

Even if you have continuous measurements, consider whether they are directly relevant. For example, benthic organisms may be responding to interstitial dissolved oxygen concentrations rather than concentrations in the water column.

Do not exclude a stressor because you do not see characteristic behaviors or symptoms in surviving organisms. The exposure may have ended, the survivors may be resistant, or the behavior or symptoms may be cryptic.

Do not exclude a stressor because no source is known or because other steps in the causal chain seem to be missing. An unknown source may be present and the exposure may result from another pathway or may result from an intermittent process.

Do not exclude a stressor because it cannot be managed. Limiting the list to candidate causes that your organization can address is efficient in terms of supporting the decision makers. However, it runs the risk of eliminating an important cause and exaggerating the importance of minor but readily remediated contributors to the impairment. It also precludes the possibility that creative options might be found for remediating causes that are not part of the a priori set of options.

Consider a Second Tier List

If you have evidence against a stressor but it is not convincing, it is often better to list it is as a low priority for analysis than to not list it at all. That increases transparency and may remind you of other possibilities if the results of the causal analysis are weak or ambiguous. Such a list of potential but unanalyzed candidate causes also might include stressors for which there are no data.